1. Field of the Invention
This invention relates to an information processing apparatus and method, a program and a recording medium, and more particularly to an information processing apparatus and method, a program and a recording medium wherein items are recommended to a user.
2. Description of the Related Art
In recent years, the communication techniques have developed and various communication services provided through a network represented by the Internet have been popularized. One of such communication services is a recommendation system which introduces recommendable contents or commodities to users in order to allow a user to appropriately select and, for example, purchase, from among a large number of contents or commodities, those contents or commodities which conform to a liking of the user.
One of such recommendation systems for contents or commodities by a computer system as described above introduces a content or a commodity selected at random. However, also a recommendation system is available which introduces contents or commodities, for example, suitable for the liking of users and estimated to be selected by the users in order to achieve more useful recommendation. In this instance, a server which performs such recommendation usually estimates the liking of users and motivates users to feed back some information thereto in order to select contents or commodities to be introduced.
The feedback information from users includes express information like, for example, five-stage evaluation from “favorable” to “unfavorable” and non-express information such as information that, in the case of music, a reproduced musical piece is favorable whereas a skipped musical piece is unfavorable. The server of the system estimates the liking of a user based on such feedback information and determines items to be presented to the user.
For the determination just described, for example, content based filtering (CBF) is available and disclosed, for example, in Japanese Patent Laid-Open No. 2001-160955 (hereinafter referred to as Patent Document 1). According to the content based filtering, where meta data are applied to each content, the liking of a user is determined as a sum total or an average of the meta data of those contents which have been enjoyed by the user. Then, the inner product or the cosine similarity degree between the liking of the user and an unknown content is used to determine whether or not the content should be recommended to the user.
Also collaborative filtering (CF) is available for the determination described above and is disclosed, for example, in P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, “GroupLens: Open Architecture for Collaborative Filtering of Netnews”, Conference on Computer Supported Cooperative Work, pp. 175-186, 1994 (hereinafter referred to as Non-Patent Document 1). According to the collaborative filtering, for example, predictive evaluation values based on similarity in content evaluation between users are used to recommend an unknown content to a user without utilizing meta data of the content.
In both methods, in order for the server to perform appropriate recommendation to a user, feedback information from more than a fixed number of users may be required. However, in an ordinary case, feedback of a great amount of information from users in a short period of time cannot be anticipated. Particularly, there is the possibility that a cold start problem that inappropriate recommendation is performed in an initial stage after operation of the system is started may occur. This is described, for example, in Maltz, D. and Ehrlich, K., “Pointing the way: Active collaborative filtering”, Proceedings of the Annual ACM SIGCHI Conference on Human Factors in Computing Systems (CHI95), pp. 202-209, 1995 (hereinafter referred to as Non-Patent Document 2).
In the method disclosed in Non-Patent Document 2, it is attempted to solve the problem by causing an existing user to transmit a pointer of information to another user. Also another method has been proposed wherein non-express feedback information of a user is obtained from residing time on a Web page or a movement of a mouse to cover express evaluation or feedback. The method is disclosed, for example, in Claypool, M., Le, P., Waseda, M. and Brown, D., “Implicit Interest Indicators”, Proceedings of the 6th International Conference on Intelligent User Interfaces, pp. 33-40, 2001 (hereinafter referred to as Non-Patent Document 3).